Clustering Data with Measurement Errors
... Approaches to clustering include statistical, machine learning, optimization and data mining perspectives. See [10, 11] for a review. In recent years probability models have been proposed as a basis for cluster analysis [1, 4, 7, 9, 15]. Methods of this type have shown promise in a number of practic ...
... Approaches to clustering include statistical, machine learning, optimization and data mining perspectives. See [10, 11] for a review. In recent years probability models have been proposed as a basis for cluster analysis [1, 4, 7, 9, 15]. Methods of this type have shown promise in a number of practic ...
Mining Quantitative Association Rules on Overlapped Intervals
... Clustering can be considered the most important unsupervised learning technique, which deals with finding a structure in a collection of unlabeled data. A cluster is therefore a collection of objects which are “similar” to each other and are “dissimilar” to the objects belonging to other clusters [8 ...
... Clustering can be considered the most important unsupervised learning technique, which deals with finding a structure in a collection of unlabeled data. A cluster is therefore a collection of objects which are “similar” to each other and are “dissimilar” to the objects belonging to other clusters [8 ...
Outlier detection 2
... K-Distance • The k-distance of p is the distance between p and its k-th nearest neighbor • In a set D of points, for any positive integer k, the k-distance of object p, denoted as kdistance(p), is the distance d(p, o) between p and an object o such that – For at least k objects o’ ∈ D \ {p}, d(p ...
... K-Distance • The k-distance of p is the distance between p and its k-th nearest neighbor • In a set D of points, for any positive integer k, the k-distance of object p, denoted as kdistance(p), is the distance d(p, o) between p and an object o such that – For at least k objects o’ ∈ D \ {p}, d(p ...
Recent Advances in Clustering: A Brief Survey
... a height-balanced tree, which stores the clustering features and it is based on two parameters: branching factor B and threshold T, which refer to the diameter of a cluster (the diameter (or radius) of each cluster must be less than T). A CF tree is built as the data is scanned. As each data point ...
... a height-balanced tree, which stores the clustering features and it is based on two parameters: branching factor B and threshold T, which refer to the diameter of a cluster (the diameter (or radius) of each cluster must be less than T). A CF tree is built as the data is scanned. As each data point ...
A Closest Fit Approach to Missing Attribute Values in Preterm Birth
... First, in the training data set, for any numerical attribute, values were sorted. Every value v was replaced by the interval [v, w), where w was the next bigger values than v in the sorted list. Our approach to discretization is the most cautious since, in the training data set, we put only one attr ...
... First, in the training data set, for any numerical attribute, values were sorted. Every value v was replaced by the interval [v, w), where w was the next bigger values than v in the sorted list. Our approach to discretization is the most cautious since, in the training data set, we put only one attr ...
A Survey on Outlier Detection Methods
... 3D spaces. • Originally outputs a label and it can be extended for scoring easily (take depth as scoring value). • For outlier detection use a global reference set. C. Deviation Based Outlier Detection • Idea used in this method is similar like classical statistical approaches (k = 1 distributions) ...
... 3D spaces. • Originally outputs a label and it can be extended for scoring easily (take depth as scoring value). • For outlier detection use a global reference set. C. Deviation Based Outlier Detection • Idea used in this method is similar like classical statistical approaches (k = 1 distributions) ...
Spatial Association Rules - Artificial Intelligence Group
... DMQL: just some syntactic sugar on top of DM algorithms? A user can formulate a DM task without paying attention to Logical and physical representation problems The correct procedural order in which some DM steps should be ...
... DMQL: just some syntactic sugar on top of DM algorithms? A user can formulate a DM task without paying attention to Logical and physical representation problems The correct procedural order in which some DM steps should be ...
A clustering algorithm using the tabu search approach
... each pattern, a random number, 0 < R < 1, is generated. If R > Pt then this pattern is assigned to cluster 2, where i is randomly generated but not the same cluster as in the current solution, 0 < i < TV, and Pt is the predefined probability threshold; otherwise it is partitioned to the same cluster ...
... each pattern, a random number, 0 < R < 1, is generated. If R > Pt then this pattern is assigned to cluster 2, where i is randomly generated but not the same cluster as in the current solution, 0 < i < TV, and Pt is the predefined probability threshold; otherwise it is partitioned to the same cluster ...
Toward a Framework for Learner Segmentation
... is carried out in O(N) time [Murtagh,1983]. For larger datasets, other methods such as the K-means may be used. While more efficient, this approach is appropriate when squared Euclidean distance is taken to be the dissimilarity measure. The Euclidean distance requires all attributes to be of the num ...
... is carried out in O(N) time [Murtagh,1983]. For larger datasets, other methods such as the K-means may be used. While more efficient, this approach is appropriate when squared Euclidean distance is taken to be the dissimilarity measure. The Euclidean distance requires all attributes to be of the num ...
A Survey on Clustering Techniques for Big Data Mining
... imperfect, complex data into usable information[1]. But, it becomes difficult to maintain huge volume of information and data day to day from many different resources and services which were not available to human space just a few decades ago. Very huge quantities of data are produced every day by a ...
... imperfect, complex data into usable information[1]. But, it becomes difficult to maintain huge volume of information and data day to day from many different resources and services which were not available to human space just a few decades ago. Very huge quantities of data are produced every day by a ...
Introduction to Computer Science
... Information stored and processed by computer is a small fragment of reality containing essential data to solve stated problem. We have to think which informations are essential, which can help us and which are completely useless. We have to think how we will represent choosen informations. The last ...
... Information stored and processed by computer is a small fragment of reality containing essential data to solve stated problem. We have to think which informations are essential, which can help us and which are completely useless. We have to think how we will represent choosen informations. The last ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.